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- 저자 : Kurina Tatiana Nikolaevna (검색결과 1 건)
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Losev Valerii Sergeevich,Panko Iuliia Vladimirovna,Kurina Tatiana Nikolaevna,Sadunova Asya Gevorgovna,Elena Kuznetcova,Kabanov Oleg Vladimirovich 대한산업공학회 2022 Industrial Engineeering & Management Systems Vol.21 No.3
Today, reverse logistics networks are increasingly expanding in various industries due to considering parameters such as environmental conditions, customer awareness, economic savings, social responsibilities, and gaining points such as customer loyalty. In the design of the reverse logistics network, the options of taking parts, repair, recycling, re-production, and disposal can be considered. In this research, an integrated forward/reverse logistics network is mod-eled and an attempt is made to provide a general model and by adding the recycling option to the previous models, to a more general optimization model that includes more recovery options. It is achieved and the network should be optimized in the forward and reverse mode at the same time, taking into account the production of various products, first with the assumption of known demand and return, and then by fuzzing the parameters with uncertainty, as well as the limited capacities of the network centers. To defuzzification of this model, a method based on alpha cut is used for cost coefficients of decision variables and the center of mass method for demand and fuzzy return in the objective function. In fuzzy limits, a method based on the comparison of fuzzy numbers is used. In order to show the application of the Mixed Integer Linear Programming results, several examples have been simulated and coded in GAMS software to obtain accurate answers. In order to solve the model in large dimensions of the problem, a multi-objective genetic algorithm has been developed and coded by MATLAB software. The results of solving the examples in both cases, the exact and near-optimal solutions obtained from the genetic algorithm, have been compared and at the end, the sensitivity analysis of the network has been done. The obtained results show that considering imprecise parameters in a fuzzy manner, firstly, a more realistic model is obtained. Secondly, in examining the answer, due to their connection, the decision maker has unlimited options for the correct decision according to the existing conditions.
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